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An effective emotion tendency perception model in empathic dialogue.

ABSTRACT: The effectiveness of open-domain dialogue systems depends heavily on emotion. In dialogue systems, previous models primarily detected emotions by looking for emotional words embedded in sentences. However, they did not precisely quantify the association of all words with emotions, which has led to a certain bias. To overcome this issue, we propose an emotion tendency perception model. The model uses an emotion encoder to accurately quantify the emotional tendencies of all words. Meanwhile, it uses a shared fusion decoder to equip the decoder with the sentiment and semantic capabilities of the encoder. We conducted extensive evaluations on Empathetic Dialogue. Experimental results demonstrate its efficacy. Compared with the state of the art, our approach has distinctive advantages.

SUBMITTER: Chen J

PROVIDER: S-EPMC10004494 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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An effective emotion tendency perception model in empathic dialogue.

Chen Jiancu J Yang Siyuan S Xiong Jiang J Xiong Yiping Y

PloS one 20230310 3

The effectiveness of open-domain dialogue systems depends heavily on emotion. In dialogue systems, previous models primarily detected emotions by looking for emotional words embedded in sentences. However, they did not precisely quantify the association of all words with emotions, which has led to a certain bias. To overcome this issue, we propose an emotion tendency perception model. The model uses an emotion encoder to accurately quantify the emotional tendencies of all words. Meanwhile, it us ...[more]

PMID: 36897862

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Project description:Affective vocalisations such as screams and laughs can convey strong emotional content without verbal information. Previous research using morphed vocalisations (e.g. 25% fear/75% anger) has revealed categorical perception of emotion in voices, showing sudden shifts at emotion category boundaries. However, it is currently unknown how further modulation of vocalisations beyond the veridical emotion (e.g. 125% fear) affects perception. Caricatured facial expressions produce emotions that are perceived as more intense and distinctive, with faster recognition relative to the original and anti-caricatured (e.g. 75% fear) emotions, but a similar effect using vocal caricatures has not been previously examined. Furthermore, caricatures can play a key role in assessing how distinctiveness is identified, in particular by evaluating accounts of emotion perception with reference to prototypes (distance from the central stimulus) and exemplars (density of the stimulus space). Stimuli consisted of four emotions (anger, disgust, fear, and pleasure) morphed at 25% intervals between a neutral expression and each emotion from 25% to 125%, and between each pair of emotions. Emotion perception was assessed using emotion intensity ratings, valence and arousal ratings, speeded categorisation and paired similarity ratings. We report two key findings: 1) across tasks, there was a strongly linear effect of caricaturing, with caricatured emotions (125%) perceived as higher in emotion intensity and arousal, and recognised faster compared to the original emotion (100%) and anti-caricatures (25%-75%); 2) our results reveal evidence for a unique contribution of a prototype-based account in emotion recognition. We show for the first time that vocal caricature effects are comparable to those found previously with facial caricatures. The set of caricatured vocalisations provided open a promising line of research for investigating vocal affect perception and emotion processing deficits in clinical populations.

Dataset Information

An effective emotion tendency perception model in empathic dialogue.

Publications

An effective emotion tendency perception model in empathic dialogue.

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